import d2lzh_pytorch as d2l
import time
import torch
from torch import nn, optim
import torch.nn.functional as F
import sys
sys.path.append("..")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


class Residual(nn.Module):
    def __init__(self, in_channels, out_channels, use_1X1conv=False, stride=1):
        super().__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels,
                               kernel_size=3, padding=1, stride=stride)
        self.conv2 = nn.Conv2d(out_channels, out_channels,
                               kernel_size=1, padding=1)
        if use_1X1conv:
            self.conv3 = nn.Conv2d(
                in_channels, out_channels, kernel_size=1, stride=stride)
        else:
            self.conv3 = None
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.bn2 = nn.BatchNorm2d(out_channels)

    def forward(self, X):
        Y = F.relu(self.bn1(self.conv1(X)))
        Y = self.bn2(self.conv2(Y))
        if self.conv3:
            X = self.conv3(X)
        return F.relu(Y+x)


blk = Residual(3, 3)
X = torch.rand(4, 3, 6, 6)
print(X.shape)
net = nn.Sequential(
    nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3),
    nn.BatchNorm2d(64),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=3, stride=2, padding == 1)
)


def resnet_block(in_channels, out_channels, num_residuals, first_block=False):
    if first_block:
        assert in_channels == out_channels
    blk = []
    for i in range(num_residuals):
        if i == 0 and not first_block:
            blk.append(Residual(in_channels, out_channels,
                                use_1X1conv=True), stride=2)
        else:
            blk.append(Residual(out_channels, out_channels))
    return nn.Sequential(*blk)


net.add_module('resnet_block1', d2l.GlobalAvgPool2d(), nn.Linear(512, 10))

X = torch.rand((1, 1, 224, 224))
for name, layer in net.parameters():
    X = layer(X)
    print(name)
    print("output shape\t", X.shape)

batch_size = 256
